NYT+Bestseller+List+Game

Cassie Lawson cassie_brooke@hotmail.com Lindsey Nettels lnettels@gmail.com ||
 * || =//New York Times// Bestseller List Game=

Overview
This online competition is designed to pit players against one another to see who can predict the rankings of the weekly //New York Times// bestseller list. Points will be based on how accurate the guesses and compiled throughout the weeks to see who really knows what America reads. Will it be //you//? In addition, book lovers will also be able to write their own reviews of books currently on the list and discuss those books in forums that will help generate more excitement for the game. Being that a number of books which have recently come out such as //Harry Potter// and //Twilight// have developed fanatical followings, it seemed like a good idea to develop a game that would allow those passionate about books to compete in a game using their knowledge of what is popular.

Instructional Objective
The objective is for learners to harness current and previous knowledge of bestselling books to predict next week's list. Players just using chance will probably not fare that well.

Learners
The game is designed for older adults, perhaps even specifically librarians, English teachers or book worms. A knowledge of previous lists is helpful, though not a prerequisite. The game is also designed to be a place for players (book lovers) to connect and discuss books they are passionate about.

Context of Use
This game would be used at home, as it is more for entertainment. Teachers might be able to use it in their classrooms with their students to predict the children's books listings. If used in school, the class can vote as a single entity, rather than individually, to limit computer requirements to just a single one. The game is designed to be played on a weekly basis, with players submitting their predictions the day before the bestseller lists are released. Players can consult any resource -- web site, library, book, etc. After the lists are released, players' scores are updated to reflect accuracy of predictions. The closer the prediction, the more points are rewarded. Ideally, players would participate individually. There is a mode for head-to-head play (à la fantasy baseball) if players only want to compete with friends, in addition to having their scores listed with all users. A single play would be the amount of time to enter predictions.

Scope
This game would consist of a website with at least seven screens. There would be two different lists to rank that you could choose from: one would be for the hardcover list and the other would be for the paperback best sellers. There would obviously be a front page to the game with links to the paperback and hardcover ranking pages. There would also be a final page where you could view the scores from the previous week's game.

The time of play for ranking a list each week would be dependent upon the player. Individuals could play by randomly ranking books without consulting any resources, or they could take time by consulting the previous weeks' rankings and then also consulting other resources such as reviews of books that are coming out. Thus, the time of play would be dependent upon the time the player would want to invest in research to improve his or her ranking.

Object of the Game
The object of this game is to be the player to match his or her ranking closest to the actual New York times bestseller list. The more matches you have with the New York times best seller list, the more points you will score in the game. In addition, you can also accumulate points through predictive ranking by predicting whether a book will make it into the top one hundred list.

Scoring

 * 100 points= you match up a prediction with the exact ranking on the New York Times bestseller list
 * 50 points= your prediction falls within the top ten list by does not match up exactly
 * 25 points= you accurately predict whether a book makes it into the top hundred

Competing Products
After spending an hour online researching book rankings, I could not find a game that actually has players try to rank books to match up with the New York Times best seller list. There are numerous sites for book lovers that have them discuss their favorite books and the current rankings on the best seller list.


 * What Should I Read Next?**



This website helps book lovers choose their next choice of book based upon those that other readers have chosen. You enter the name of the book that you have just finished, and then based upon that listing it will compile a list of suggested readings that other readers have chosen after reading that particular book.

aNobil is a website that allows you to share your thoughts on books you have currently read and discuss them with others in discussion groups.
 * aNobil**

There are numerous other websites that allow one to meet other readers and discuss their favorite book online. They also allow readers to create their own top lists of books; however, I could not find any website that provided a simulation for ranking books.

Design Details
//Universal Elements// The overall feel of the game will be realistic-looking and minimalist with muted colors. The site itself will be designed to look like pages from the actual New York Times newspaper. Pages will be simple and contain no flash elements.

//Specific Elements// There will first be an introduction page which users have to click on in order to enter the game. The main game page will then feature several tabs where users can view previous list predictions, enter in the upcoming week's predictions, view head-to-head results, research books, and view information on other users. There might also be tabs for a page on which users can write their own reviews of the books currently on the best seller list. This page would then have links to discussion boards where users could discuss the books as well. Another tab might link to a page just for predictive ranking where users could decide if a book that has just been released will even make it to the bestseller list.

Website Pages

 * //Your Profile//- a page where players can share information about themselves such as their favorite books, books they are currently reading, etc. It will also show their points total.
 * //Previous Predictions//- This page will contain links all of the weeks that players logged on and made predictions.
 * //All User Predictions//- This page will allow users to browse by user name through the predictions lists of other players.
 * //Best Sellers//- This page will list reviews of books from the New York times that are currently in the top ten list. It will also provide links to reviews on other pages. In addition, the page will list release dates of books scheduled to come out by week.
 * //New York Times//- The website will also link to the New York Times website on which players can do their own research as well.
 * //Prediction Page//- This page will consist of a series of drop down menus from which players can predict the books that will ,ak the top ten list. A mockup of the prediction page is shown below.

//Technical Elements// This game would be designed for internet play only. It could be created using a simple html editor such as Dreamweaver. An example mockup for the game could be the following.

Motivational Issues
The main motivational incentive for this game stems from Keller and Suzuki's ARCS model, and it is competition. This whole game of ranking allows one to compete not only against other players, but also in one to one competitions and against oneself. Players are competing to see if they can do better than all of the other players, and to see if they can better than the prior week's score. Their main competition is against the New York Times' Best Seller list. All of this would provide a great deal of stimulus to encourage players to do the best research they can in order to outwit opponents. This would also tie into the ARCS notion of challenge as well. Players will obviously be challenged to do better than their opponents, but they will also be challenged to do better than themselves. And of course, it will also tie into the element of confidence built into the ARCS model. As players accumulate points and guess correctly each week, they will also build up their sense of confidence as well.

This game probably would not tie into the notion of flow. Though, if players spent an extensive amount of time researching books and lists in order to try and do the best ranking job possible, they could enter into an area of flow while browsing through websites.

Design Process
The process for designing this game began with ideas about competitive online games that involved predicting the stock market or box office totals being presented during the 670 class. This lead to thinking about what else could be predicted on a regular basis. And being an English teacher, my love of reading wanted to be a part of this project. This was the first and only idea I had for this assignment and am happy to see it grow. Background information was gathered through internet searches. As far as can be determined, there are not games like this one. Feedback was received via meetings with Bernie Dodge and by speaking to friends and family about the idea. There is not a playable prototype, only mock screen shots. The lessons learned during this process was to not be afraid to work with someone else.

Another student's game actually influenced the development of this one as well. Looking at Sean Harvey's game //Popular Groove// along with a number of websites that allow book lovers to connect and discuss common books, it seemed natural to add an element to the game that would allow players to share their own reviews of the books. We decided to not give players points for the reviews, but rather keep points to the correct rankings that players make. Allowing players to write their own reviews and discuss the books on discussion boards instead will allow players to form a sense of community and bond with each other. It also might make the element of competition involved with the game more fun if players are competing against friends they have made on the discussion boards.

Also, we decided to keep the webpage very simple and straightforward, mimicking The New York Times newspaper itself, because we wanted it to be extremely easy for users to navigate. Since we have assumed that players will be middle-aged to older adults, we wanted the user interface to be extremely easy for them to move from page to page.